# Independently Recurrent Neural Networks

Simple TensorFlow implementation of Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN by Shuai Li et al. The author's original implementation in Theano and Lasagne can be found in Sunnydreamrain/IndRNN_Theano_Lasagne.

## Summary

In IndRNNs, neurons in recurrent layers are independent from each other. The basic RNN calculates the hidden state `h`

with `h = act(W * input + U * state + b)`

. IndRNNs use an element-wise vector multiplication `u * state`

meaning each neuron has a single recurrent weight connected to its last hidden state.

The IndRNN

- can be used efficiently with ReLU activation functions making it easier to stack multiple recurrent layers without saturating gradients
- allows for better interpretability, as neurons in the same layer are independent from each other
- prevents vanishing and exploding gradients by regulating each neuron's recurrent weight

## Usage

Copy ind_rnn_cell.py into your project.

```
from ind_rnn_cell import IndRNNCell
# Regulate each neuron's recurrent weight as recommended in the paper
recurrent_max = pow(2, 1 / TIME_STEPS)
cell = MultiRNNCell([IndRNNCell(128, recurrent_max_abs=recurrent_max),
IndRNNCell(128, recurrent_max_abs=recurrent_max)])
output, state = tf.nn.dynamic_rnn(cell, input_data, dtype=tf.float32)
...
```

## Experiments in the paper

### Addition Problem

See examples/addition_rnn.py for a script reproducing the "Adding Problem" from the paper. Below are the results reproduced with the `addition_rnn.py`

code.

### Sequential MNIST

See examples/sequential_mnist.py for a script reproducing the Sequential MNIST experiment. I let it run for two days and stopped it after 60,000 training steps with a

- Training error rate of 0.7%
- Validation error rate of 1.1%
**Test error rate of 1.1%**

## Requirements

- Python 3.4+
- TensorFlow 1.5+